wroking make!

1 files changed, 31 insertions, 26 deletions

diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py
index edfb781..2a42dc0 100644
--- a/docs/source/auto_examples/plot_OT_2D_samples.py
+++ b/docs/source/auto_examples/plot_OT_2D_samples.py
@@ -4,57 +4,60 @@
 2D Optimal transport between empirical distributions
 ====================================================
 
-@author: rflamary
 """
 
+# Author: Remi Flamary <remi.flamary@unice.fr>
+#
+# License: MIT License
+
 import numpy as np
 import matplotlib.pylab as pl
 import ot
 
 #%% parameters and data generation
 
-n=50 # nb samples
+n = 50  # nb samples
 
-mu_s=np.array([0,0])
-cov_s=np.array([[1,0],[0,1]])
+mu_s = np.array([0, 0])
+cov_s = np.array([[1, 0], [0, 1]])
 
-mu_t=np.array([4,4])
-cov_t=np.array([[1,-.8],[-.8,1]])
+mu_t = np.array([4, 4])
+cov_t = np.array([[1, -.8], [-.8, 1]])
 
-xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)
-xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)
+xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)
+xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t)
 
-a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples
+a, b = np.ones((n,)) / n, np.ones((n,)) / n  # uniform distribution on samples
 
 # loss matrix
-M=ot.dist(xs,xt)
-M/=M.max()
+M = ot.dist(xs, xt)
+M /= M.max()
 
 #%% plot samples
 
 pl.figure(1)
-pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')
-pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')
+pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
 pl.legend(loc=0)
-pl.title('Source and traget distributions')
+pl.title('Source and target distributions')
 
 pl.figure(2)
-pl.imshow(M,interpolation='nearest')
+pl.imshow(M, interpolation='nearest')
 pl.title('Cost matrix M')
 
 
 #%% EMD
 
-G0=ot.emd(a,b,M)
+G0 = ot.emd(a, b, M)
 
 pl.figure(3)
-pl.imshow(G0,interpolation='nearest')
+pl.imshow(G0, interpolation='nearest')
 pl.title('OT matrix G0')
 
 pl.figure(4)
-ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])
-pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')
-pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')
+ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])
+pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
 pl.legend(loc=0)
 pl.title('OT matrix with samples')
 
@@ -62,17 +65,19 @@ pl.title('OT matrix with samples')
 #%% sinkhorn
 
 # reg term
-lambd=5e-4
+lambd = 1e-3
 
-Gs=ot.sinkhorn(a,b,M,lambd)
+Gs = ot.sinkhorn(a, b, M, lambd)
 
 pl.figure(5)
-pl.imshow(Gs,interpolation='nearest')
+pl.imshow(Gs, interpolation='nearest')
 pl.title('OT matrix sinkhorn')
 
 pl.figure(6)
-ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])
-pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')
-pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')
+ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])
+pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
 pl.legend(loc=0)
 pl.title('OT matrix Sinkhorn with samples')
+
+pl.show()